'''
Created on 2017年10月25日

@author: hjdong
'''

from numpy import *
import operator
import  os
from os import listdir

def dict2list(dic:dict):
    ''' 将字典转化为列表 '''
    keys = dic.keys()
    vals = dic.values()
    lst = [(key, val) for key, val in zip(keys, vals)]
    return lst

def classify0(inX, dataSet, labels, k):
    dataSetSize = dataSet.shape[0]
    diffMat = tile(inX, (dataSetSize,1)) - dataSet
    sqDiffMat = diffMat**2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances**0.5
    sortedDistIndicies = distances.argsort()     
    classCount={}          
    for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i]]
        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
    sortedClassCount = sorted(dict2list(classCount), key=lambda x:x[1], reverse=True)
    return sortedClassCount[0][0]

def createDataSet():
    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
    labels = ['A','A','B','B']
    return group, labels

def file2matrix(filename):
    fr = open(filename)
    numberOfLines = len(fr.readlines())         #get the number of lines in the file
    returnMat = zeros((numberOfLines,3))        #prepare matrix to return
    classLabelVector = []                       #prepare labels return   
    fr = open(filename)
    index = 0
    for line in fr.readlines():
        line = line.strip()
        listFromLine = line.split('\t')
        returnMat[index,:] = listFromLine[0:3]
        classLabelVector.append(int(listFromLine[-1]))
        index += 1
    return returnMat,classLabelVector
    
def autoNorm(dataSet):
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    ranges = maxVals - minVals
    normDataSet = zeros(shape(dataSet))
    m = dataSet.shape[0]
    normDataSet = dataSet - tile(minVals, (m,1))
    normDataSet = normDataSet/tile(ranges, (m,1))   #element wise divide
    return normDataSet, ranges, minVals
   
def datingClassTest():
    hoRatio = 0.10      #剔除10%的数据
    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')       #加载数据集
    normMat, ranges, minVals = autoNorm(datingDataMat)  #归一化
    m = normMat.shape[0]            #获取行数
    numTestVecs = int(m*hoRatio)    #抽取测试集
    errorCount = 0.0
    for i in range(numTestVecs):
        classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3)
        print("the classifier came back with: {0}, the real answer is: {1}".format(classifierResult, datingLabels[i]))
        if(classifierResult != datingLabels[i]): 
            errorCount += 1.0
    print("the total error rate is: {0}".format(errorCount/float(numTestVecs)))
    print(errorCount)


# datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')       #load data setfrom file
# normMat, ranges, minVals = autoNorm(datingDataMat)
# print(normMat)
# print(ranges)
# print(minVals)
# print(os.path.abspath("."))
datingClassTest()